{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 安装第三方库\n",
    "!pip install statsmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.sandbox.regression.predstd import wls_prediction_std\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>7.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>9.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>11.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>10.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>12.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14</td>\n",
       "      <td>14.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15</td>\n",
       "      <td>15.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16</td>\n",
       "      <td>16.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17</td>\n",
       "      <td>18.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>18</td>\n",
       "      <td>16.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>19</td>\n",
       "      <td>18.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>20</td>\n",
       "      <td>18.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>21</td>\n",
       "      <td>22.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>22</td>\n",
       "      <td>19.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>23</td>\n",
       "      <td>22.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>24</td>\n",
       "      <td>22.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>25</td>\n",
       "      <td>25.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>26</td>\n",
       "      <td>25.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>27</td>\n",
       "      <td>28.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>28</td>\n",
       "      <td>28.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     x      y\n",
       "0   10   7.70\n",
       "1   10   9.87\n",
       "2   11  11.18\n",
       "3   12  10.43\n",
       "4   13  12.36\n",
       "5   14  14.15\n",
       "6   15  15.73\n",
       "7   16  16.40\n",
       "8   17  18.86\n",
       "9   18  16.13\n",
       "10  19  18.21\n",
       "11  20  18.37\n",
       "12  21  22.61\n",
       "13  22  19.83\n",
       "14  23  22.67\n",
       "15  24  22.70\n",
       "16  25  25.16\n",
       "17  26  25.55\n",
       "18  27  28.21\n",
       "19  28  28.12"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取并展示数据\n",
    "data = pd.read_csv('./data/simple_example.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(    const   x\n",
       " 0     1.0  10\n",
       " 1     1.0  10\n",
       " 2     1.0  11\n",
       " 3     1.0  12\n",
       " 4     1.0  13\n",
       " 5     1.0  14\n",
       " 6     1.0  15\n",
       " 7     1.0  16\n",
       " 8     1.0  17\n",
       " 9     1.0  18\n",
       " 10    1.0  19\n",
       " 11    1.0  20\n",
       " 12    1.0  21\n",
       " 13    1.0  22\n",
       " 14    1.0  23\n",
       " 15    1.0  24\n",
       " 16    1.0  25\n",
       " 17    1.0  26\n",
       " 18    1.0  27\n",
       " 19    1.0  28,\n",
       "         y\n",
       " 0    7.70\n",
       " 1    9.87\n",
       " 2   11.18\n",
       " 3   10.43\n",
       " 4   12.36\n",
       " 5   14.15\n",
       " 6   15.73\n",
       " 7   16.40\n",
       " 8   18.86\n",
       " 9   16.13\n",
       " 10  18.21\n",
       " 11  18.37\n",
       " 12  22.61\n",
       " 13  19.83\n",
       " 14  22.67\n",
       " 15  22.70\n",
       " 16  25.16\n",
       " 17  25.55\n",
       " 18  28.21\n",
       " 19  28.12)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 数据准备\n",
    "features, labels = ['x'], ['y']\n",
    "Y = data[labels]\n",
    "# 加入常量变量\n",
    "X = sm.add_constant(data[features])\n",
    "X, Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x7ff40f3debb0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 构建并训练模型\n",
    "model = sm.OLS(Y, X)\n",
    "re = model.fit()\n",
    "re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.962\n",
      "Model:                            OLS   Adj. R-squared:                  0.960\n",
      "Method:                 Least Squares   F-statistic:                     460.5\n",
      "Date:                Thu, 02 Nov 2023   Prob (F-statistic):           2.85e-14\n",
      "Time:                        19:22:24   Log-Likelihood:                -31.374\n",
      "No. Observations:                  20   AIC:                             66.75\n",
      "Df Residuals:                      18   BIC:                             68.74\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -0.9495      0.934     -1.017      0.323      -2.912       1.013\n",
      "x              1.0330      0.048     21.458      0.000       0.932       1.134\n",
      "==============================================================================\n",
      "Omnibus:                        0.745   Durbin-Watson:                   2.345\n",
      "Prob(Omnibus):                  0.689   Jarque-Bera (JB):                0.673\n",
      "Skew:                           0.074   Prob(JB):                        0.714\n",
      "Kurtosis:                       2.113   Cond. No.                         66.3\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# 整体统计分析结果\n",
    "print(re.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "检验假设x的系数等于0：\n",
      "<F test: F=460.4584822034311, p=2.848465414495694e-14, df_denom=18, df_num=1>\n"
     ]
    }
   ],
   "source": [
    "# 用f_test检测x对应的系数a是否显著\n",
    "print('检验假设x的系数等于0：')\n",
    "print(re.f_test('x=0'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "检测假设const的系数等于0：\n",
      "<F test: F=1.0335579388583023, p=0.3227956400831507, df_denom=18, df_num=1>\n"
     ]
    }
   ],
   "source": [
    "# 用f_test检测常量b是否显著\n",
    "print('检测假设const的系数等于0：')\n",
    "print(re.f_test('const=0'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "检测假设x的系数等于1和const的系数等于0同时成立：\n",
      "<F test: F=0.9965463122444933, p=0.388626797606395, df_denom=18, df_num=2>\n"
     ]
    }
   ],
   "source": [
    "# 用f_test检测a=1, b=0同时成立的显著性\n",
    "print('检测假设x的系数等于1和const的系数等于0同时成立：')\n",
    "print(re.f_test(['x=1', 'const=0']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                 OLS Regression Results                                \n",
      "=======================================================================================\n",
      "Dep. Variable:                      y   R-squared (uncentered):                   0.996\n",
      "Model:                            OLS   Adj. R-squared (uncentered):              0.996\n",
      "Method:                 Least Squares   F-statistic:                              4876.\n",
      "Date:                Thu, 02 Nov 2023   Prob (F-statistic):                    2.26e-24\n",
      "Time:                        19:22:24   Log-Likelihood:                         -31.933\n",
      "No. Observations:                  20   AIC:                                      65.87\n",
      "Df Residuals:                      19   BIC:                                      66.86\n",
      "Df Model:                           1                                                  \n",
      "Covariance Type:            nonrobust                                                  \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "x              0.9862      0.014     69.825      0.000       0.957       1.016\n",
      "==============================================================================\n",
      "Omnibus:                        0.489   Durbin-Watson:                   2.218\n",
      "Prob(Omnibus):                  0.783   Jarque-Bera (JB):                0.561\n",
      "Skew:                           0.033   Prob(JB):                        0.755\n",
      "Kurtosis:                       2.182   Cond. No.                         1.00\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
      "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# const并不显著，去掉这个常量变量\n",
    "# 构建新的模型\n",
    "model_new = sm.OLS(Y, data[features])\n",
    "re_new = model_new.fit()\n",
    "# 输出新模型的分析结果\n",
    "print(re_new.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 计算预测结果的标准差，预测下界，预测上界\n",
    "pre_std, pre_low, pre_up = wls_prediction_std(re_new, alpha=0.05)\n",
    "# 为在Matplotlib中显示中文，设置特殊字体\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "plt.rcParams.update({'font.size': 13})\n",
    "# 创建一个图形框\n",
    "fig = plt.figure(figsize=(6, 6), dpi=100)\n",
    "# 在图形框里只画一幅图\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_title('线性回归统计分析示例')\n",
    "ax.set_xlabel('$x$')\n",
    "ax.set_ylabel('$y$')\n",
    "# 画点图，用蓝色圆点表示原始数据\n",
    "ax.scatter(data[features], data[labels], color='b', label='真实值: $y = x + \\epsilon$')\n",
    "# 画线图，用红色虚线表示95%置信区间\n",
    "ax.plot(data[features], pre_up, 'r--', label='95%置信区间')\n",
    "ax.plot(data[features], re_new.predict(data[features]), color='r',\n",
    "        label=f'预测值: $y = {re_new.params[features].item():.3f}x$')\n",
    "ax.plot(data[features], pre_low, 'r--')\n",
    "# 设置图例的样式\n",
    "legend = plt.legend(shadow=True)\n",
    "legend.get_frame().set_facecolor('#6F93AE')\n",
    "plt.savefig('linear_stat.png', dpi=200)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
